Abstract
A robotic car, also known as a driverless or self-driving car, is an autonomous vehicle capable of sensing its environment and navigating itself without human input. Robotic cars exist mainly as prototypes and demonstration systems, but are likely to become more widespread in the near future. Usually, autonomous vehicles sense their surroundings for lane detection and obstacle avoidance with radar, 2D LIDAR, 3D LIDAR, and camera sensors.
This article describes our approach in developing an affordable stereo vision system using two ordinary webcams and OpenCV (Open Source Computer Vision) library. We applied our stereo vision system to Vulture 2 and iWheels robotic platforms to enter IGVC (Intelligent Ground Vehicle Competition) 2012 and 2013. The results show that stereo vision based navigation is a promising and affordable mechanism to develop robot cars.
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References
Theisen, B.L., Nguyen, D.: The 14th Annual Intelligent Ground Vehicle Competition: intelligent teams creating itelligent ground robots. In: Intelligent Robots and Computer Vision XXIV: Algorithms, Boston, MA (2006)
Kosinski, A., Nguyen, D., Theisen, B.: Intelligent Ground Vehicle Competition, http://www.igvc.org/ (accessed August 19, 2013)
Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)
Zhang, Z.: Flexible camera calibration by viewing a plane from unknown orientations. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision (1999)
Clarke, T.A., Fryer, J.G.: The development of camera calibration methods and models. The Photogrammetric Record 16(91), 51–66 (1998)
Wang, F.-Y.: A simple and analytical procedure for calibrating extrinsic camera parameters. IEEE Transactions on Robotics and Automation 20(1), 121–124 (2004)
Konolige, K.: Small Vision Systems: Hardware and Implementation. In: Robotics Research, pp. 203-212 (1998)
Hirschmuller, H.: Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2 (2005)
Scharstein, D., Szeliski, R.: Middlebury Stereo Vision Page, http://vision.middlebury.edu/stereo/ (accessed August 19, 2013)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47, 7–42 (2002)
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© 2014 Springer International Publishing Switzerland
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Kawatsu, C., Li, J., Chung, C.J. (2014). Obstacle & Lane Detection and Local Path Planning for IGVC Robotic Vehicles Using Stereo Vision. In: Kim, JH., Matson, E., Myung, H., Xu, P., Karray, F. (eds) Robot Intelligence Technology and Applications 2. Advances in Intelligent Systems and Computing, vol 274. Springer, Cham. https://doi.org/10.1007/978-3-319-05582-4_57
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DOI: https://doi.org/10.1007/978-3-319-05582-4_57
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05581-7
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